---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: eo_train1-10_eval11
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# eo_train1-10_eval11

This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6932
- Accuracy: 0.5

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 128
- eval_batch_size: 128
- seed: 7658372
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 3000

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log        | 0      | 0    | 2.6863          | 0.0      |
| 0.6284        | 100.0  | 100  | 0.7859          | 0.5      |
| 0.548         | 200.0  | 200  | 0.7353          | 0.5      |
| 0.5022        | 300.0  | 300  | 0.6964          | 0.5      |
| 0.6935        | 400.0  | 400  | 0.6936          | 0.5      |
| 0.6933        | 500.0  | 500  | 0.6933          | 0.5      |
| 0.6932        | 600.0  | 600  | 0.6932          | 0.5      |
| 0.6932        | 700.0  | 700  | 0.6932          | 0.5      |
| 0.6932        | 800.0  | 800  | 0.6932          | 0.5      |
| 0.6932        | 900.0  | 900  | 0.6932          | 0.5      |
| 0.6932        | 1000.0 | 1000 | 0.6932          | 0.5      |
| 0.6932        | 1100.0 | 1100 | 0.6932          | 0.5      |
| 0.6932        | 1200.0 | 1200 | 0.6932          | 0.5      |
| 0.6932        | 1300.0 | 1300 | 0.6932          | 0.5      |
| 0.6932        | 1400.0 | 1400 | 0.6932          | 0.5      |
| 0.6932        | 1500.0 | 1500 | 0.6932          | 0.5      |
| 0.6932        | 1600.0 | 1600 | 0.6932          | 0.5      |
| 0.6932        | 1700.0 | 1700 | 0.6932          | 0.5      |
| 0.6932        | 1800.0 | 1800 | 0.6932          | 0.5      |
| 0.6932        | 1900.0 | 1900 | 0.6932          | 0.5      |
| 0.6932        | 2000.0 | 2000 | 0.6932          | 0.5      |
| 0.6932        | 2100.0 | 2100 | 0.6932          | 0.5      |
| 0.6932        | 2200.0 | 2200 | 0.6932          | 0.5      |
| 0.6932        | 2300.0 | 2300 | 0.6932          | 0.5      |
| 0.6932        | 2400.0 | 2400 | 0.6932          | 0.5      |
| 0.6932        | 2500.0 | 2500 | 0.6932          | 0.5      |
| 0.6932        | 2600.0 | 2600 | 0.6932          | 0.5      |
| 0.6932        | 2700.0 | 2700 | 0.6932          | 0.5      |
| 0.6932        | 2800.0 | 2800 | 0.6932          | 0.5      |
| 0.6932        | 2900.0 | 2900 | 0.6932          | 0.5      |
| 0.6932        | 3000.0 | 3000 | 0.6932          | 0.5      |


### Framework versions

- Transformers 4.46.0
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.1